Classical morphological operators can be hard to train due to the non linearity, as pointed out in [1]}, where the authors proposed to clip the gradients of these layers and apply specific learning on these layers. Hence integrating them into a NAS framework can be challenging, this is due to the fact that NAS framework will try to learn the best architecture with the set of operations. So we cannot build a hand-designed architecture that will stabilize the new layers. Hence the new layers must be stable to any architecture. So we proposed a stable version that we are going to introduce in this section.
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